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Record W2006155966 · doi:10.3141/1804-10

Time-Use Metadata

2002· article· en· W2006155966 on OpenAlexaff
Andrew S. Harvey

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsMetadataComparabilityComputer scienceTask (project management)Work (physics)Metadata repositoryField (mathematics)Data scienceTransport engineeringWorld Wide WebEngineering

Abstract

fetched live from OpenAlex

Time-diary data provide a complete sequential record of all activities of individuals, including travel, for a period of 24 or 48 h or longer. Hence, time-use data have much to offer travel behavior analysts and modelers. The pool of time-use data is rapidly increasing. Additionally, comparability between time-use data and travel data is growing, largely because of the expanding volume of activity data collected in travel surveys. One challenge is to ensure that the data and time-use, travel, and other researchers can be brought together in the most efficient manner. This task requires the development of both study-level and variable-level metadata standards. Much work, providing a basis for the development of time-use metadata standards, has already been undertaken in collateral fields. Arguments are made for exploration, application, and expansion of existing work, to establish time-use metadata standards. A consolidation of efforts is proposed between time-use and travel behavior data professionals to ensure that each field has the optimum opportunity to identify, locate, evaluate, and access useful data in either field.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.002
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.167
GPT teacher head0.404
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2002
Admission routes1
Has abstractyes

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